In [1]:
import json
from glob import glob
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
from torch.utils.data.sampler import SubsetRandomSampler, WeightedRandomSampler
import torchvision
from torchvision import transforms, datasets
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import csv
import time
from sklearn.model_selection import train_test_split
In [2]:
training_path = "./Data/Training"
test_path = "./Data/Test"
sign_label_path = "./Data/sign_labels.csv"
batch_size = 64
random_seed = 42
class_count = 43 # 43 classes in GTSRB
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
In [3]:
def showLabelDistribution(dataset, dataloader=None):
labelDict = dict.fromkeys(range(class_count), 0)
print(labelDict)
if(dataloader == None):
for i in range(len(dataset)):
label = int(dataset[i][1])
labelDict[label] = labelDict[label] + 1
else:
for _, (images, labels) in enumerate(dataloader):
for label in labels:
label = int(label)
labelDict[label] += 1
print(labelDict)
plt.bar(labelDict.keys(), labelDict.values(), width=1.0, color='g')
plt.show()
In [4]:
def getSignLabels():
sign_labels = pd.read_csv(sign_label_path)
sign_object = {}
for i, row in sign_labels.iterrows():
sign_object[str(row[0])] = row[1]
return sign_object
In [5]:
# The German Traffic Sign Recognition Benchmark
#
# sample code for reading the traffic sign images and the
# corresponding labels
#
# example:
#
# trainImages, trainLabels = readTrafficSigns('GTSRB/Training')
# print len(trainLabels), len(trainImages)
# plt.imshow(trainImages[42])
# plt.show()
#
# have fun, Christian
# Source: http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset#Downloads
# function for reading the images
# arguments: path to the traffic sign data, for example './GTSRB/Training'
# returns: list of images, list of corresponding labels
def readTrafficSigns(rootpath):
'''Reads traffic sign data for German Traffic Sign Recognition Benchmark.
Arguments: path to the traffic sign data, for example './GTSRB/Training'
Returns: list of images, list of corresponding labels'''
images = [] # images
labels = [] # corresponding labels
# loop over all 42 classes
for c in range(0,class_count):
prefix = rootpath + '/' + format(c, '05d') + '/' # subdirectory for class
gtFile = open(prefix + 'GT-'+ format(c, '05d') + '.csv') # annotations file
gtReader = csv.reader(gtFile, delimiter=';') # csv parser for annotations file
next(gtReader) # skip header
# loop over all images in current annotations file
for row in gtReader:
images.append(plt.imread(prefix + row[0])) # the 1th column is the filename
labels.append(row[7]) # the 8th column is the label
gtFile.close()
return images, labels
In [6]:
class TrafficSignDataset(data.Dataset):
def __init__(self, images, labels):
self.images, self.labels = images, labels
self.transforms = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((32, 32)),
# Samples are randomly perturbed in position ([-2,2] pixels),
# in scale ([.9,1.1] ratio) and rotation ([-15,+15] degrees).
# Source: http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf
transforms.RandomApply([
transforms.RandomRotation(15, resample=Image.BICUBIC),
transforms.RandomAffine(0, translate=(
0.2, 0.2), resample=Image.BICUBIC),
transforms.RandomAffine(0, shear=20, resample=Image.BICUBIC),
transforms.RandomAffine(0, scale=(0.9, 1.1),
resample=Image.BICUBIC)
]),
transforms.ToTensor(),
transforms.Normalize((0.3337, 0.3064, 0.3171), ( 0.2672, 0.2564, 0.2629))
])
def __getitem__(self, index):
image = self.transforms(self.images[index])
labels = np.asarray(self.labels[index])
labels = torch.from_numpy(labels.astype('int'))
return image, labels
def __len__(self):
return len(self.images)
In [7]:
# Assign Random Seed
np.random.seed(random_seed)
In [8]:
# Loading training data
images, labels = readTrafficSigns(training_path)
# Splitting the data to 8:2 ratios
train_images, val_images, train_labels, val_labels = train_test_split(images, labels, test_size=0.2, random_state=1)
In [9]:
training_dataset = TrafficSignDataset(train_images, train_labels)
validation_dataset = TrafficSignDataset(val_images, val_labels)
In [10]:
print(len(training_dataset))
print(len(validation_dataset))
31367 7842
In [11]:
# Check the distribution of labels
showLabelDistribution(training_dataset)
showLabelDistribution(validation_dataset)
{0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, 37: 0, 38: 0, 39: 0, 40: 0, 41: 0, 42: 0}
{0: 161, 1: 1774, 2: 1795, 3: 1131, 4: 1582, 5: 1483, 6: 336, 7: 1148, 8: 1142, 9: 1186, 10: 1623, 11: 1077, 12: 1658, 13: 1714, 14: 633, 15: 510, 16: 340, 17: 869, 18: 944, 19: 158, 20: 297, 21: 268, 22: 317, 23: 410, 24: 214, 25: 1204, 26: 471, 27: 200, 28: 452, 29: 215, 30: 348, 31: 621, 32: 193, 33: 562, 34: 345, 35: 969, 36: 301, 37: 172, 38: 1634, 39: 247, 40: 280, 41: 195, 42: 188}
{0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, 37: 0, 38: 0, 39: 0, 40: 0, 41: 0, 42: 0}
{0: 49, 1: 446, 2: 455, 3: 279, 4: 398, 5: 377, 6: 84, 7: 292, 8: 268, 9: 284, 10: 387, 11: 243, 12: 442, 13: 446, 14: 147, 15: 120, 16: 80, 17: 241, 18: 256, 19: 52, 20: 63, 21: 62, 22: 73, 23: 100, 24: 56, 25: 296, 26: 129, 27: 40, 28: 88, 29: 55, 30: 102, 31: 159, 32: 47, 33: 127, 34: 75, 35: 231, 36: 89, 37: 38, 38: 436, 39: 53, 40: 80, 41: 45, 42: 52}
In [31]:
# Even out the class distribution for training set
classidx = np.bincount(training_dataset.labels)
weights = 1 / np.array([classidx[int(y)] for y in training_dataset.labels])
sampler = WeightedRandomSampler(weights, len(weights) * class_count)
In [32]:
# Splitting the Dataset into Training Dataloader and the Validation Dataloader
train_loader = torch.utils.data.DataLoader(training_dataset, batch_size=batch_size, sampler=sampler, num_workers=4, drop_last=True)
validation_loader = torch.utils.data.DataLoader(validation_dataset, batch_size=batch_size, num_workers=4)
In [14]:
# Make the distribution more even for better training results
showLabelDistribution(_, train_loader)
# showLabelDistribution(_, validation_loader)
{0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, 37: 0, 38: 0, 39: 0, 40: 0, 41: 0, 42: 0}
{0: 31481, 1: 31383, 2: 31310, 3: 31367, 4: 31378, 5: 31506, 6: 31104, 7: 31465, 8: 30968, 9: 31592, 10: 31382, 11: 31554, 12: 31331, 13: 31430, 14: 31210, 15: 31158, 16: 31652, 17: 31543, 18: 31154, 19: 31729, 20: 31286, 21: 31366, 22: 31482, 23: 31418, 24: 31439, 25: 31135, 26: 31564, 27: 31290, 28: 31304, 29: 31374, 30: 31316, 31: 31209, 32: 31408, 33: 31101, 34: 31475, 35: 31483, 36: 31080, 37: 31442, 38: 31561, 39: 30940, 40: 31493, 41: 31343, 42: 31530}
In [15]:
for i, (images, labels) in enumerate(train_loader):
print(images.shape)
print(labels)
break
torch.Size([64, 3, 32, 32])
tensor([16, 31, 22, 14, 8, 11, 8, 17, 5, 11, 18, 38, 27, 37, 33, 21, 22, 3,
20, 23, 3, 1, 13, 1, 28, 25, 14, 2, 12, 19, 8, 20, 18, 11, 40, 19,
35, 13, 33, 36, 25, 8, 1, 42, 31, 6, 37, 25, 12, 42, 11, 23, 19, 39,
19, 0, 25, 39, 25, 33, 26, 18, 18, 34])
In [33]:
class TrafficSignNet(nn.Module):
# Batch shape is (3,32,32)
def __init__(self):
super(TrafficSignNet, self).__init__()
# prevent overfitting by dropping randomly
self.dropout = nn.Dropout(p=0.5)
# convolutional 100 maps of 42x42 neurons 7x7
self.conv1 = nn.Conv2d(3, 100, 7)
# max pooling 100 maps of 21x21 neurons 2x2
self.batch1 = nn.BatchNorm2d(100)
self.pool = nn.MaxPool2d(2)
# convolutional 150 maps of 18x18 neurons 4x4
self.conv2 = nn.Conv2d(100, 150, 4)
# max pooling 150 maps of 9x9 neurons 2x2
self.batch2 = nn.BatchNorm2d(150)
# use self.pool; Same pool kernal
# convolutional 250 maps of 6x6 neurons 4x4
self.conv3 = nn.Conv2d(150, 250, 4)
# max pooling 250 maps of 3x3 neurons 2x2
self.batch3 = nn.BatchNorm2d(250)
# use self.pool; Same pool kernal
# fully connected 300 neurons 1x1
self.fc1 = nn.Linear(250 * 1 * 1, 300)
self.batch4 = nn.BatchNorm1d(300)
# fully connected 43 neurons 1x1
self.fc2 = nn.Linear(300, 43)
def forward(self, x):
### convolutional 100 maps of 42x42 neurons 7x7
x = self.conv1(x)
# ELU activation function
x = F.elu(x)
# max pooling 100 maps of 21x21 neurons 2x2
x = self.pool(x)
x = self.batch1(x)
# prevent overfitting by dropping randomly
x = self.dropout(x)
### convolutional 150 maps of 18x18 neurons 4x4
x = self.conv2(x)
# ELU activation function
x = F.elu(x)
# max pooling 150 maps of 9x9 neurons 2x2
x = self.pool(x)
x = self.batch2(x)
# prevent overfitting by dropping randomly
x = self.dropout(x)
### convolutional 250 maps of 6x6 neurons 4x4
x = self.conv3(x)
# ELU activation function
x = F.elu(x)
# max pooling 250 maps of 3x3 neurons 2x2
x = self.pool(x)
x = self.batch3(x)
# prevent overfitting by dropping randomly
x = self.dropout(x)
### fully connected 300 neurons 1x1
x = x.view(-1, 250 * 1 * 1)
x = self.fc1(x)
# ELU activation function
x = F.elu(x)
# prevent overfitting by dropping randomly
x = self.batch4(x)
x = self.dropout(x)
### fully connected 43 neurons 1x1
x = self.fc2(x)
return x
In [34]:
print(device)
cuda
In [35]:
net = TrafficSignNet()
net.to(device)
Out[35]:
TrafficSignNet( (dropout): Dropout(p=0.5, inplace=False) (conv1): Conv2d(3, 100, kernel_size=(7, 7), stride=(1, 1)) (batch1): BatchNorm2d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (conv2): Conv2d(100, 150, kernel_size=(4, 4), stride=(1, 1)) (batch2): BatchNorm2d(150, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(150, 250, kernel_size=(4, 4), stride=(1, 1)) (batch3): BatchNorm2d(250, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (fc1): Linear(in_features=250, out_features=300, bias=True) (batch4): BatchNorm1d(300, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (fc2): Linear(in_features=300, out_features=43, bias=True) )
In [36]:
num_epochs = 100
params = [p for p in net.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.0001, momentum=0.9, weight_decay=0.0005)
loss_function = nn.CrossEntropyLoss()
correct = 0
total = 0
consecutive_loss_increase = 0
prev_loss = np.Inf
running_loss = 0
train_losses = []
train_nums = []
val_losses = []
val_nums = []
training_accuracy_epoch = dict.fromkeys(range(100), [])
running_losses_epoch = dict.fromkeys(range(100), [])
training_losses_epoch = dict.fromkeys(range(100), [])
val_losses_epoch = dict.fromkeys(range(100), [])
In [ ]:
for epoch in range(num_epochs):
t0 = time.time()
##### Training
print("Epoch %d: " % (epoch +1))
net.train()
t1 = time.time()
for i, (images, labels) in enumerate(train_loader):
images, labels = images.to(device), labels.to(device)
# forward + backward + optimize
loss = loss_function(net(images), labels)
loss.backward()
optimizer.step()
# zero the parameter gradients
optimizer.zero_grad()
train_losses.append(loss.item())
train_nums.append(len(images))
running_loss += loss.item()
if i % 2000 == 1999:
running_losses_epoch[epoch].append(running_loss / 2000)
print('[%d, %5d] running loss: %.3f\t took: %.6f seconds' %
(epoch + 1, i + 1, running_loss / 2000, time.time() - t1))
running_loss = 0.0
t1 = time.time()
training_loss = np.sum(np.multiply(train_losses, train_nums)) / np.sum(train_nums)
training_losses_epoch[epoch].append(training_loss)
print('[%d] training loss: %.3f\t' %
(epoch + 1, training_loss))
##### Evaluate
net.eval()
with torch.no_grad():
for i, (images, labels) in enumerate(validation_loader):
images, labels = images.to(device), labels.to(device)
outputs = net(images)
loss = loss_function(outputs, labels)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
val_losses.append(loss.item())
val_nums.append(len(images))
if i % 10 == 9:
validation_accuracy = np.sum(correct) / total
training_accuracy_epoch[epoch].append(validation_accuracy)
print('[%d, %5d] Validation Accuracy: %.3f \t' % (epoch + 1, i + 1, validation_accuracy))
val_losses_epoch[epoch].append(new_loss)
new_loss = np.sum(np.multiply(val_losses, val_nums)) / np.sum(val_nums)
print('Validation Loss: %.3f \t' % (new_loss))
if new_loss <= prev_loss:
# save progress each valid epoch
torch.save(net.state_dict(), './checkpoint/' + str(epoch) + '.pth')
prev_loss = new_loss
consecutive_loss_increase = 0
else:
consecutive_loss_increase += 1
if(consecutive_loss_increase > 9):
print("Ending training due to consecutive non-decrease of loss")
print('Epoch %d: took: %.3f seconds' % (epoch + 1, time.time() - t0))
torch.save(net.state_dict(), './final_model.pth')
Epoch 1: [1, 2000] running loss: 3.324 took: 55.365425 seconds [1, 4000] running loss: 2.692 took: 55.085633 seconds [1, 6000] running loss: 2.325 took: 55.110111 seconds [1, 8000] running loss: 2.056 took: 55.239632 seconds [1, 10000] running loss: 1.861 took: 55.045239 seconds [1, 12000] running loss: 1.715 took: 54.935344 seconds [1, 14000] running loss: 1.588 took: 54.999887 seconds [1, 16000] running loss: 1.490 took: 54.999984 seconds [1, 18000] running loss: 1.395 took: 55.085250 seconds [1, 20000] running loss: 1.328 took: 55.166181 seconds [1] training loss: 1.942 [1, 10] Validation Accuracy: 0.706 [1, 20] Validation Accuracy: 0.715 [1, 30] Validation Accuracy: 0.714 [1, 40] Validation Accuracy: 0.715 [1, 50] Validation Accuracy: 0.712 [1, 60] Validation Accuracy: 0.711 [1, 70] Validation Accuracy: 0.709 [1, 80] Validation Accuracy: 0.706 [1, 90] Validation Accuracy: 0.708 [1, 100] Validation Accuracy: 0.708 [1, 110] Validation Accuracy: 0.709 [1, 120] Validation Accuracy: 0.709 Validation Loss: 1.061 Epoch 1: took: 583.449 seconds Epoch 2: [2, 2000] running loss: 1.918 took: 55.652858 seconds [2, 4000] running loss: 1.180 took: 55.095329 seconds [2, 6000] running loss: 1.136 took: 55.199572 seconds [2, 8000] running loss: 1.093 took: 54.726820 seconds [2, 10000] running loss: 1.048 took: 54.571121 seconds [2, 12000] running loss: 1.021 took: 54.347553 seconds [2, 14000] running loss: 0.985 took: 54.682309 seconds [2, 16000] running loss: 0.952 took: 54.498756 seconds [2, 18000] running loss: 0.924 took: 54.622391 seconds [2, 20000] running loss: 0.902 took: 54.566911 seconds [2] training loss: 1.491 [2, 10] Validation Accuracy: 0.718 [2, 20] Validation Accuracy: 0.725 [2, 30] Validation Accuracy: 0.733 [2, 40] Validation Accuracy: 0.739 [2, 50] Validation Accuracy: 0.745 [2, 60] Validation Accuracy: 0.750 [2, 70] Validation Accuracy: 0.754 [2, 80] Validation Accuracy: 0.759 [2, 90] Validation Accuracy: 0.763 [2, 100] Validation Accuracy: 0.767 [2, 110] Validation Accuracy: 0.769 [2, 120] Validation Accuracy: 0.771 Validation Loss: 0.844 Epoch 2: took: 580.119 seconds Epoch 3: [3, 2000] running loss: 1.338 took: 55.306299 seconds [3, 4000] running loss: 0.845 took: 54.409333 seconds [3, 6000] running loss: 0.822 took: 54.316359 seconds [3, 8000] running loss: 0.798 took: 54.560393 seconds [3, 10000] running loss: 0.779 took: 54.590781 seconds [3, 12000] running loss: 0.767 took: 54.448799 seconds [3, 14000] running loss: 0.747 took: 54.486918 seconds [3, 16000] running loss: 0.729 took: 54.546095 seconds [3, 18000] running loss: 0.715 took: 54.538340 seconds [3, 20000] running loss: 0.702 took: 54.545234 seconds [3] training loss: 1.251 [3, 10] Validation Accuracy: 0.776 [3, 20] Validation Accuracy: 0.779 [3, 30] Validation Accuracy: 0.784 [3, 40] Validation Accuracy: 0.787 [3, 50] Validation Accuracy: 0.791 [3, 60] Validation Accuracy: 0.794 [3, 70] Validation Accuracy: 0.796 [3, 80] Validation Accuracy: 0.799 [3, 90] Validation Accuracy: 0.802 [3, 100] Validation Accuracy: 0.804 [3, 110] Validation Accuracy: 0.805 [3, 120] Validation Accuracy: 0.807 Validation Loss: 0.715 Epoch 3: took: 577.837 seconds Epoch 4: [4, 2000] running loss: 1.048 took: 55.054522 seconds [4, 4000] running loss: 0.659 took: 54.600202 seconds [4, 6000] running loss: 0.646 took: 54.661488 seconds [4, 8000] running loss: 0.638 took: 54.566534 seconds [4, 10000] running loss: 0.625 took: 54.518240 seconds [4, 12000] running loss: 0.614 took: 54.676773 seconds [4, 14000] running loss: 0.603 took: 54.506539 seconds [4, 16000] running loss: 0.587 took: 54.451468 seconds [4, 18000] running loss: 0.581 took: 54.458856 seconds [4, 20000] running loss: 0.568 took: 54.601957 seconds [4] training loss: 1.093 [4, 10] Validation Accuracy: 0.811 [4, 20] Validation Accuracy: 0.813 [4, 30] Validation Accuracy: 0.816 [4, 40] Validation Accuracy: 0.819 [4, 50] Validation Accuracy: 0.821 [4, 60] Validation Accuracy: 0.823 [4, 70] Validation Accuracy: 0.826 [4, 80] Validation Accuracy: 0.828 [4, 90] Validation Accuracy: 0.830 [4, 100] Validation Accuracy: 0.832 [4, 110] Validation Accuracy: 0.833 [4, 120] Validation Accuracy: 0.835 Validation Loss: 0.617 Epoch 4: took: 578.102 seconds Epoch 5: [5, 2000] running loss: 0.852 took: 55.183833 seconds [5, 4000] running loss: 0.545 took: 54.619906 seconds [5, 6000] running loss: 0.531 took: 54.552154 seconds [5, 8000] running loss: 0.523 took: 54.732851 seconds [5, 10000] running loss: 0.508 took: 54.427655 seconds [5, 12000] running loss: 0.503 took: 54.740324 seconds [5, 14000] running loss: 0.489 took: 54.759813 seconds [5, 16000] running loss: 0.486 took: 54.771776 seconds [5, 18000] running loss: 0.477 took: 54.535389 seconds [5, 20000] running loss: 0.474 took: 54.536787 seconds [5] training loss: 0.975 [5, 10] Validation Accuracy: 0.838 [5, 20] Validation Accuracy: 0.840 [5, 30] Validation Accuracy: 0.842 [5, 40] Validation Accuracy: 0.844 [5, 50] Validation Accuracy: 0.845 [5, 60] Validation Accuracy: 0.847 [5, 70] Validation Accuracy: 0.849 [5, 80] Validation Accuracy: 0.850 [5, 90] Validation Accuracy: 0.852 [5, 100] Validation Accuracy: 0.853 [5, 110] Validation Accuracy: 0.855 [5, 120] Validation Accuracy: 0.856 Validation Loss: 0.541 Epoch 5: took: 579.020 seconds Epoch 6: [6, 2000] running loss: 0.704 took: 55.178056 seconds [6, 4000] running loss: 0.446 took: 54.577684 seconds [6, 6000] running loss: 0.440 took: 54.393411 seconds [6, 8000] running loss: 0.433 took: 54.566937 seconds [6, 10000] running loss: 0.420 took: 54.420261 seconds [6, 12000] running loss: 0.417 took: 54.446282 seconds [6, 14000] running loss: 0.412 took: 54.653894 seconds [6, 16000] running loss: 0.405 took: 54.485290 seconds [6, 18000] running loss: 0.399 took: 54.556020 seconds [6, 20000] running loss: 0.399 took: 54.609950 seconds [6] training loss: 0.883 [6, 10] Validation Accuracy: 0.858 [6, 20] Validation Accuracy: 0.860 [6, 30] Validation Accuracy: 0.861 [6, 40] Validation Accuracy: 0.863 [6, 50] Validation Accuracy: 0.864 [6, 60] Validation Accuracy: 0.866 [6, 70] Validation Accuracy: 0.867 [6, 80] Validation Accuracy: 0.868 [6, 90] Validation Accuracy: 0.870 [6, 100] Validation Accuracy: 0.871 [6, 110] Validation Accuracy: 0.872 [6, 120] Validation Accuracy: 0.873 Validation Loss: 0.481 Epoch 6: took: 578.070 seconds Epoch 7: [7, 2000] running loss: 0.591 took: 55.084676 seconds [7, 4000] running loss: 0.375 took: 54.698761 seconds [7, 6000] running loss: 0.379 took: 54.354683 seconds [7, 8000] running loss: 0.366 took: 54.745421 seconds [7, 10000] running loss: 0.360 took: 54.588702 seconds [7, 12000] running loss: 0.361 took: 54.556895 seconds [7, 14000] running loss: 0.356 took: 54.586300 seconds [7, 16000] running loss: 0.348 took: 54.570473 seconds [7, 18000] running loss: 0.348 took: 54.620752 seconds [7, 20000] running loss: 0.345 took: 54.679221 seconds [7] training loss: 0.808 [7, 10] Validation Accuracy: 0.875 [7, 20] Validation Accuracy: 0.876 [7, 30] Validation Accuracy: 0.877 [7, 40] Validation Accuracy: 0.879 [7, 50] Validation Accuracy: 0.880 [7, 60] Validation Accuracy: 0.881 [7, 70] Validation Accuracy: 0.882 [7, 80] Validation Accuracy: 0.883 [7, 90] Validation Accuracy: 0.884 [7, 100] Validation Accuracy: 0.885 [7, 110] Validation Accuracy: 0.886 [7, 120] Validation Accuracy: 0.887 Validation Loss: 0.433 Epoch 7: took: 578.655 seconds Epoch 8: [8, 2000] running loss: 0.515 took: 55.254648 seconds [8, 4000] running loss: 0.332 took: 54.699014 seconds [8, 6000] running loss: 0.328 took: 54.716403 seconds [8, 8000] running loss: 0.324 took: 54.522190 seconds [8, 10000] running loss: 0.316 took: 54.585575 seconds [8, 12000] running loss: 0.314 took: 54.374440 seconds [8, 14000] running loss: 0.314 took: 54.559377 seconds [8, 16000] running loss: 0.303 took: 54.652973 seconds [8, 18000] running loss: 0.307 took: 54.366357 seconds [8, 20000] running loss: 0.302 took: 54.194113 seconds [8] training loss: 0.747 [8, 10] Validation Accuracy: 0.888 [8, 20] Validation Accuracy: 0.889 [8, 30] Validation Accuracy: 0.890 [8, 40] Validation Accuracy: 0.891 [8, 50] Validation Accuracy: 0.892 [8, 60] Validation Accuracy: 0.893 [8, 70] Validation Accuracy: 0.894 [8, 80] Validation Accuracy: 0.894 [8, 90] Validation Accuracy: 0.895 [8, 100] Validation Accuracy: 0.896 [8, 110] Validation Accuracy: 0.897 [8, 120] Validation Accuracy: 0.897 Validation Loss: 0.395 Epoch 8: took: 577.983 seconds Epoch 9: [9, 2000] running loss: 0.457 took: 55.269054 seconds [9, 4000] running loss: 0.299 took: 54.726003 seconds [9, 6000] running loss: 0.294 took: 54.457182 seconds [9, 8000] running loss: 0.290 took: 54.642346 seconds [9, 10000] running loss: 0.288 took: 54.473087 seconds [9, 12000] running loss: 0.285 took: 54.640692 seconds [9, 14000] running loss: 0.281 took: 54.610149 seconds [9, 16000] running loss: 0.280 took: 54.609021 seconds [9, 18000] running loss: 0.277 took: 54.800296 seconds [9, 20000] running loss: 0.274 took: 54.403246 seconds [9] training loss: 0.696 [9, 10] Validation Accuracy: 0.898 [9, 20] Validation Accuracy: 0.899 [9, 30] Validation Accuracy: 0.900 [9, 40] Validation Accuracy: 0.901 [9, 50] Validation Accuracy: 0.901 [9, 60] Validation Accuracy: 0.902 [9, 70] Validation Accuracy: 0.903 [9, 80] Validation Accuracy: 0.903 [9, 90] Validation Accuracy: 0.904 [9, 100] Validation Accuracy: 0.905 [9, 110] Validation Accuracy: 0.905 [9, 120] Validation Accuracy: 0.905 Validation Loss: 0.364 Epoch 9: took: 578.771 seconds Epoch 10: [10, 2000] running loss: 0.413 took: 55.153833 seconds [10, 4000] running loss: 0.268 took: 54.863665 seconds [10, 6000] running loss: 0.265 took: 54.698804 seconds [10, 8000] running loss: 0.259 took: 54.663857 seconds [10, 10000] running loss: 0.262 took: 54.544959 seconds [10, 12000] running loss: 0.262 took: 54.771718 seconds [10, 14000] running loss: 0.260 took: 54.572846 seconds [10, 16000] running loss: 0.257 took: 54.653345 seconds [10, 18000] running loss: 0.255 took: 54.365498 seconds [10, 20000] running loss: 0.250 took: 54.692506 seconds [10] training loss: 0.652 [10, 10] Validation Accuracy: 0.906 [10, 20] Validation Accuracy: 0.907 [10, 30] Validation Accuracy: 0.908 [10, 40] Validation Accuracy: 0.908 [10, 50] Validation Accuracy: 0.909 [10, 60] Validation Accuracy: 0.910 [10, 70] Validation Accuracy: 0.910 [10, 80] Validation Accuracy: 0.911 [10, 90] Validation Accuracy: 0.911 [10, 100] Validation Accuracy: 0.912 [10, 110] Validation Accuracy: 0.913 [10, 120] Validation Accuracy: 0.913 Validation Loss: 0.338 Epoch 10: took: 579.136 seconds Epoch 11: [11, 2000] running loss: 0.382 took: 55.197771 seconds [11, 4000] running loss: 0.247 took: 54.552434 seconds [11, 6000] running loss: 0.247 took: 54.628771 seconds [11, 8000] running loss: 0.245 took: 54.488700 seconds [11, 10000] running loss: 0.245 took: 54.530061 seconds [11, 12000] running loss: 0.240 took: 54.687970 seconds [11, 14000] running loss: 0.235 took: 54.586473 seconds [11, 16000] running loss: 0.234 took: 54.464891 seconds [11, 18000] running loss: 0.234 took: 54.609008 seconds [11, 20000] running loss: 0.230 took: 54.489753 seconds [11] training loss: 0.615 [11, 10] Validation Accuracy: 0.914 [11, 20] Validation Accuracy: 0.914 [11, 30] Validation Accuracy: 0.915 [11, 40] Validation Accuracy: 0.915 [11, 50] Validation Accuracy: 0.916 [11, 60] Validation Accuracy: 0.916 [11, 70] Validation Accuracy: 0.917 [11, 80] Validation Accuracy: 0.917 [11, 90] Validation Accuracy: 0.918 [11, 100] Validation Accuracy: 0.918 [11, 110] Validation Accuracy: 0.919 [11, 120] Validation Accuracy: 0.919 Validation Loss: 0.315 Epoch 11: took: 578.381 seconds Epoch 12: [12, 2000] running loss: 0.355 took: 55.406961 seconds [12, 4000] running loss: 0.227 took: 54.579539 seconds [12, 6000] running loss: 0.225 took: 54.603438 seconds [12, 8000] running loss: 0.227 took: 54.576296 seconds [12, 10000] running loss: 0.224 took: 54.684395 seconds [12, 12000] running loss: 0.220 took: 54.283154 seconds [12, 14000] running loss: 0.222 took: 54.271317 seconds [12, 16000] running loss: 0.221 took: 54.036002 seconds [12, 18000] running loss: 0.218 took: 54.484673 seconds [12, 20000] running loss: 0.215 took: 53.998393 seconds [12] training loss: 0.582 [12, 10] Validation Accuracy: 0.920 [12, 20] Validation Accuracy: 0.920 [12, 30] Validation Accuracy: 0.920 [12, 40] Validation Accuracy: 0.921 [12, 50] Validation Accuracy: 0.921 [12, 60] Validation Accuracy: 0.922 [12, 70] Validation Accuracy: 0.922 [12, 80] Validation Accuracy: 0.923 [12, 90] Validation Accuracy: 0.923 [12, 100] Validation Accuracy: 0.924 [12, 110] Validation Accuracy: 0.924 [12, 120] Validation Accuracy: 0.924 Validation Loss: 0.296 Epoch 12: took: 576.904 seconds Epoch 13: [13, 2000] running loss: 0.330 took: 54.915875 seconds [13, 4000] running loss: 0.215 took: 54.250108 seconds [13, 6000] running loss: 0.211 took: 54.231112 seconds [13, 8000] running loss: 0.211 took: 54.185534 seconds [13, 10000] running loss: 0.208 took: 54.313503 seconds [13, 12000] running loss: 0.208 took: 54.097151 seconds [13, 14000] running loss: 0.205 took: 54.220961 seconds [13, 16000] running loss: 0.205 took: 54.280343 seconds [13, 18000] running loss: 0.204 took: 54.225591 seconds [13, 20000] running loss: 0.202 took: 54.150935 seconds [13] training loss: 0.553 [13, 10] Validation Accuracy: 0.925 [13, 20] Validation Accuracy: 0.925 [13, 30] Validation Accuracy: 0.926 [13, 40] Validation Accuracy: 0.926 [13, 50] Validation Accuracy: 0.926 [13, 60] Validation Accuracy: 0.927 [13, 70] Validation Accuracy: 0.927 [13, 80] Validation Accuracy: 0.927 [13, 90] Validation Accuracy: 0.928 [13, 100] Validation Accuracy: 0.928 [13, 110] Validation Accuracy: 0.929 [13, 120] Validation Accuracy: 0.929 Validation Loss: 0.278 Epoch 13: took: 574.915 seconds Epoch 14: [14, 2000] running loss: 0.306 took: 54.869262 seconds [14, 4000] running loss: 0.202 took: 54.128273 seconds [14, 6000] running loss: 0.201 took: 54.407545 seconds [14, 8000] running loss: 0.197 took: 54.255926 seconds [14, 10000] running loss: 0.196 took: 54.232670 seconds [14, 12000] running loss: 0.195 took: 54.289680 seconds [14, 14000] running loss: 0.194 took: 54.255805 seconds [14, 16000] running loss: 0.194 took: 54.175142 seconds [14, 18000] running loss: 0.193 took: 54.366341 seconds [14, 20000] running loss: 0.193 took: 54.244624 seconds [14] training loss: 0.528 [14, 10] Validation Accuracy: 0.929 [14, 20] Validation Accuracy: 0.930 [14, 30] Validation Accuracy: 0.930 [14, 40] Validation Accuracy: 0.930 [14, 50] Validation Accuracy: 0.931 [14, 60] Validation Accuracy: 0.931 [14, 70] Validation Accuracy: 0.931 [14, 80] Validation Accuracy: 0.932 [14, 90] Validation Accuracy: 0.932 [14, 100] Validation Accuracy: 0.933 [14, 110] Validation Accuracy: 0.933 [14, 120] Validation Accuracy: 0.933 Validation Loss: 0.262 Epoch 14: took: 575.191 seconds Epoch 15: [15, 2000] running loss: 0.295 took: 55.382302 seconds [15, 4000] running loss: 0.186 took: 54.511506 seconds [15, 6000] running loss: 0.188 took: 54.442546 seconds [15, 8000] running loss: 0.188 took: 54.256869 seconds [15, 10000] running loss: 0.188 took: 54.245740 seconds [15, 12000] running loss: 0.186 took: 54.442271 seconds [15, 14000] running loss: 0.185 took: 54.261034 seconds [15, 16000] running loss: 0.184 took: 54.334732 seconds [15, 18000] running loss: 0.182 took: 54.482610 seconds [15, 20000] running loss: 0.182 took: 54.423307 seconds [15] training loss: 0.505 [15, 10] Validation Accuracy: 0.934 [15, 20] Validation Accuracy: 0.934 [15, 30] Validation Accuracy: 0.934 [15, 40] Validation Accuracy: 0.934 [15, 50] Validation Accuracy: 0.935 [15, 60] Validation Accuracy: 0.935 [15, 70] Validation Accuracy: 0.935 [15, 80] Validation Accuracy: 0.936 [15, 90] Validation Accuracy: 0.936 [15, 100] Validation Accuracy: 0.936 [15, 110] Validation Accuracy: 0.936 [15, 120] Validation Accuracy: 0.937 Validation Loss: 0.249 Epoch 15: took: 576.754 seconds Epoch 16: [16, 2000] running loss: 0.283 took: 55.011872 seconds [16, 4000] running loss: 0.179 took: 54.319456 seconds [16, 6000] running loss: 0.179 took: 54.165416 seconds [16, 8000] running loss: 0.177 took: 54.027640 seconds [16, 10000] running loss: 0.177 took: 54.321780 seconds [16, 12000] running loss: 0.179 took: 54.054215 seconds [16, 14000] running loss: 0.181 took: 54.166602 seconds [16, 16000] running loss: 0.176 took: 54.195304 seconds [16, 18000] running loss: 0.175 took: 54.096394 seconds [16, 20000] running loss: 0.173 took: 54.056750 seconds [16] training loss: 0.484 [16, 10] Validation Accuracy: 0.937 [16, 20] Validation Accuracy: 0.937 [16, 30] Validation Accuracy: 0.938 [16, 40] Validation Accuracy: 0.938 [16, 50] Validation Accuracy: 0.938 [16, 60] Validation Accuracy: 0.938 [16, 70] Validation Accuracy: 0.939 [16, 80] Validation Accuracy: 0.939 [16, 90] Validation Accuracy: 0.939 [16, 100] Validation Accuracy: 0.939 [16, 110] Validation Accuracy: 0.940 [16, 120] Validation Accuracy: 0.940 Validation Loss: 0.237 Epoch 16: took: 574.277 seconds Epoch 17: [17, 2000] running loss: 0.265 took: 54.998935 seconds [17, 4000] running loss: 0.172 took: 54.191339 seconds [17, 6000] running loss: 0.171 took: 54.231598 seconds [17, 8000] running loss: 0.168 took: 54.037665 seconds [17, 10000] running loss: 0.171 took: 54.264779 seconds [17, 12000] running loss: 0.168 took: 54.064018 seconds [17, 14000] running loss: 0.168 took: 53.964598 seconds [17, 16000] running loss: 0.167 took: 54.317823 seconds [17, 18000] running loss: 0.170 took: 54.225549 seconds [17, 20000] running loss: 0.165 took: 54.122939 seconds [17] training loss: 0.466 [17, 10] Validation Accuracy: 0.940 [17, 20] Validation Accuracy: 0.940 [17, 30] Validation Accuracy: 0.941 [17, 40] Validation Accuracy: 0.941 [17, 50] Validation Accuracy: 0.941 [17, 60] Validation Accuracy: 0.941 [17, 70] Validation Accuracy: 0.942 [17, 80] Validation Accuracy: 0.942 [17, 90] Validation Accuracy: 0.942 [17, 100] Validation Accuracy: 0.942 [17, 110] Validation Accuracy: 0.943 [17, 120] Validation Accuracy: 0.943 Validation Loss: 0.226 Epoch 17: took: 574.417 seconds Epoch 18: [18, 2000] running loss: 0.251 took: 55.010586 seconds [18, 4000] running loss: 0.163 took: 54.096925 seconds [18, 6000] running loss: 0.166 took: 54.235383 seconds [18, 8000] running loss: 0.163 took: 54.214638 seconds [18, 10000] running loss: 0.163 took: 54.113555 seconds [18, 12000] running loss: 0.162 took: 54.035511 seconds [18, 14000] running loss: 0.161 took: 54.207373 seconds [18, 16000] running loss: 0.161 took: 54.061690 seconds [18, 18000] running loss: 0.157 took: 54.305658 seconds [18, 20000] running loss: 0.159 took: 54.057842 seconds [18] training loss: 0.449 [18, 10] Validation Accuracy: 0.943 [18, 20] Validation Accuracy: 0.943 [18, 30] Validation Accuracy: 0.944 [18, 40] Validation Accuracy: 0.944 [18, 50] Validation Accuracy: 0.944 [18, 60] Validation Accuracy: 0.944 [18, 70] Validation Accuracy: 0.944 [18, 80] Validation Accuracy: 0.945 [18, 90] Validation Accuracy: 0.945 [18, 100] Validation Accuracy: 0.945 [18, 110] Validation Accuracy: 0.945 [18, 120] Validation Accuracy: 0.945 Validation Loss: 0.216 Epoch 18: took: 574.330 seconds Epoch 19: [19, 2000] running loss: 0.243 took: 55.013799 seconds [19, 4000] running loss: 0.157 took: 54.352978 seconds [19, 6000] running loss: 0.159 took: 54.147119 seconds [19, 8000] running loss: 0.158 took: 54.122629 seconds [19, 10000] running loss: 0.155 took: 54.167569 seconds [19, 12000] running loss: 0.157 took: 54.108205 seconds [19, 14000] running loss: 0.157 took: 54.171517 seconds [19, 16000] running loss: 0.154 took: 54.130116 seconds [19, 18000] running loss: 0.153 took: 54.276578 seconds [19, 20000] running loss: 0.154 took: 54.093378 seconds [19] training loss: 0.434 [19, 10] Validation Accuracy: 0.946 [19, 20] Validation Accuracy: 0.946 [19, 30] Validation Accuracy: 0.946 [19, 40] Validation Accuracy: 0.946 [19, 50] Validation Accuracy: 0.947 [19, 60] Validation Accuracy: 0.947 [19, 70] Validation Accuracy: 0.947 [19, 80] Validation Accuracy: 0.947 [19, 90] Validation Accuracy: 0.947 [19, 100] Validation Accuracy: 0.948 [19, 110] Validation Accuracy: 0.948 [19, 120] Validation Accuracy: 0.948 Validation Loss: 0.207 Epoch 19: took: 574.570 seconds Epoch 20: [20, 2000] running loss: 0.234 took: 54.933059 seconds [20, 4000] running loss: 0.155 took: 54.349551 seconds [20, 6000] running loss: 0.151 took: 53.987488 seconds [20, 8000] running loss: 0.153 took: 53.994540 seconds [20, 10000] running loss: 0.147 took: 53.968621 seconds [20, 12000] running loss: 0.148 took: 53.934291 seconds [20, 14000] running loss: 0.151 took: 54.113923 seconds [20, 16000] running loss: 0.147 took: 54.111339 seconds [20, 18000] running loss: 0.148 took: 54.026915 seconds [20, 20000] running loss: 0.148 took: 53.940190 seconds [20] training loss: 0.419 [20, 10] Validation Accuracy: 0.948 [20, 20] Validation Accuracy: 0.948 [20, 30] Validation Accuracy: 0.949 [20, 40] Validation Accuracy: 0.949 [20, 50] Validation Accuracy: 0.949 [20, 60] Validation Accuracy: 0.949 [20, 70] Validation Accuracy: 0.949 [20, 80] Validation Accuracy: 0.950 [20, 90] Validation Accuracy: 0.950 [20, 100] Validation Accuracy: 0.950 [20, 110] Validation Accuracy: 0.950 [20, 120] Validation Accuracy: 0.950 Validation Loss: 0.199 Epoch 20: took: 573.326 seconds Epoch 21: [21, 2000] running loss: 0.223 took: 54.776526 seconds [21, 4000] running loss: 0.149 took: 54.237749 seconds [21, 6000] running loss: 0.147 took: 54.221108 seconds [21, 8000] running loss: 0.144 took: 54.253715 seconds [21, 10000] running loss: 0.148 took: 54.309309 seconds [21, 12000] running loss: 0.146 took: 53.960535 seconds [21, 14000] running loss: 0.146 took: 54.087516 seconds [21, 16000] running loss: 0.145 took: 54.205639 seconds [21, 18000] running loss: 0.140 took: 54.198158 seconds [21, 20000] running loss: 0.144 took: 53.963370 seconds [21] training loss: 0.406 [21, 10] Validation Accuracy: 0.950 [21, 20] Validation Accuracy: 0.951 [21, 30] Validation Accuracy: 0.951 [21, 40] Validation Accuracy: 0.951 [21, 50] Validation Accuracy: 0.951 [21, 60] Validation Accuracy: 0.951 [21, 70] Validation Accuracy: 0.951 [21, 80] Validation Accuracy: 0.952 [21, 90] Validation Accuracy: 0.952 [21, 100] Validation Accuracy: 0.952 [21, 110] Validation Accuracy: 0.952 [21, 120] Validation Accuracy: 0.952 Validation Loss: 0.192 Epoch 21: took: 574.474 seconds Epoch 22: [22, 2000] running loss: 0.218 took: 54.972653 seconds [22, 4000] running loss: 0.143 took: 54.382946 seconds [22, 6000] running loss: 0.143 took: 53.999898 seconds [22, 8000] running loss: 0.143 took: 54.208167 seconds [22, 10000] running loss: 0.141 took: 54.042443 seconds [22, 12000] running loss: 0.140 took: 54.025456 seconds [22, 14000] running loss: 0.140 took: 54.292968 seconds [22, 16000] running loss: 0.141 took: 54.201307 seconds [22, 18000] running loss: 0.141 took: 54.309771 seconds [22, 20000] running loss: 0.137 took: 54.041453 seconds [22] training loss: 0.394 [22, 10] Validation Accuracy: 0.952 [22, 20] Validation Accuracy: 0.953 [22, 30] Validation Accuracy: 0.953 [22, 40] Validation Accuracy: 0.953 [22, 50] Validation Accuracy: 0.953 [22, 60] Validation Accuracy: 0.953 [22, 70] Validation Accuracy: 0.953 [22, 80] Validation Accuracy: 0.953 [22, 90] Validation Accuracy: 0.954 [22, 100] Validation Accuracy: 0.954 [22, 110] Validation Accuracy: 0.954 [22, 120] Validation Accuracy: 0.954 Validation Loss: 0.185 Epoch 22: took: 574.367 seconds Epoch 23: [23, 2000] running loss: 0.215 took: 54.923558 seconds [23, 4000] running loss: 0.137 took: 54.172319 seconds [23, 6000] running loss: 0.136 took: 54.204645 seconds [23, 8000] running loss: 0.135 took: 54.276155 seconds [23, 10000] running loss: 0.138 took: 54.167450 seconds [23, 12000] running loss: 0.137 took: 54.254482 seconds [23, 14000] running loss: 0.138 took: 53.993859 seconds [23, 16000] running loss: 0.136 took: 53.866908 seconds [23, 18000] running loss: 0.136 took: 54.195473 seconds [23, 20000] running loss: 0.137 took: 54.170383 seconds [23] training loss: 0.383 [23, 10] Validation Accuracy: 0.954 [23, 20] Validation Accuracy: 0.954 [23, 30] Validation Accuracy: 0.955 [23, 40] Validation Accuracy: 0.955 [23, 50] Validation Accuracy: 0.955 [23, 60] Validation Accuracy: 0.955 [23, 70] Validation Accuracy: 0.955 [23, 80] Validation Accuracy: 0.955 [23, 90] Validation Accuracy: 0.955 [23, 100] Validation Accuracy: 0.956 [23, 110] Validation Accuracy: 0.956 [23, 120] Validation Accuracy: 0.956 Validation Loss: 0.178 Epoch 23: took: 574.349 seconds Epoch 24: [24, 2000] running loss: 0.209 took: 54.945344 seconds [24, 4000] running loss: 0.135 took: 54.296894 seconds [24, 6000] running loss: 0.133 took: 54.262574 seconds [24, 8000] running loss: 0.132 took: 54.203007 seconds [24, 10000] running loss: 0.134 took: 54.127643 seconds [24, 12000] running loss: 0.135 took: 53.961265 seconds [24, 14000] running loss: 0.130 took: 54.031863 seconds [24, 16000] running loss: 0.133 took: 54.124175 seconds [24, 18000] running loss: 0.132 took: 54.158474 seconds [24, 20000] running loss: 0.133 took: 54.004940 seconds [24] training loss: 0.373 [24, 10] Validation Accuracy: 0.956 [24, 20] Validation Accuracy: 0.956 [24, 30] Validation Accuracy: 0.956 [24, 40] Validation Accuracy: 0.956 [24, 50] Validation Accuracy: 0.957 [24, 60] Validation Accuracy: 0.957 [24, 70] Validation Accuracy: 0.957 [24, 80] Validation Accuracy: 0.957 [24, 90] Validation Accuracy: 0.957 [24, 100] Validation Accuracy: 0.957 [24, 110] Validation Accuracy: 0.957 [24, 120] Validation Accuracy: 0.957 Validation Loss: 0.173 Epoch 24: took: 574.075 seconds Epoch 25: [25, 2000] running loss: 0.202 took: 54.861640 seconds [25, 4000] running loss: 0.132 took: 54.309481 seconds [25, 6000] running loss: 0.129 took: 54.143412 seconds [25, 8000] running loss: 0.130 took: 54.218457 seconds [25, 10000] running loss: 0.128 took: 54.147207 seconds [25, 12000] running loss: 0.130 took: 53.958697 seconds [25, 14000] running loss: 0.127 took: 54.116635 seconds [25, 16000] running loss: 0.125 took: 53.983289 seconds [25, 18000] running loss: 0.128 took: 54.346887 seconds [25, 20000] running loss: 0.131 took: 54.233449 seconds [25] training loss: 0.363 [25, 10] Validation Accuracy: 0.958 [25, 20] Validation Accuracy: 0.958 [25, 30] Validation Accuracy: 0.958 [25, 40] Validation Accuracy: 0.958 [25, 50] Validation Accuracy: 0.958 [25, 60] Validation Accuracy: 0.958 [25, 70] Validation Accuracy: 0.958 [25, 80] Validation Accuracy: 0.958 [25, 90] Validation Accuracy: 0.959 [25, 100] Validation Accuracy: 0.959 [25, 110] Validation Accuracy: 0.959 [25, 120] Validation Accuracy: 0.959 Validation Loss: 0.167 Epoch 25: took: 574.386 seconds Epoch 26: [26, 2000] running loss: 0.197 took: 54.775665 seconds [26, 4000] running loss: 0.126 took: 54.386510 seconds [26, 6000] running loss: 0.125 took: 54.132597 seconds [26, 8000] running loss: 0.126 took: 54.292469 seconds [26, 10000] running loss: 0.126 took: 54.226598 seconds [26, 12000] running loss: 0.126 took: 53.941896 seconds [26, 14000] running loss: 0.126 took: 54.103243 seconds [26, 16000] running loss: 0.124 took: 54.132822 seconds [26, 18000] running loss: 0.126 took: 54.143661 seconds [26, 20000] running loss: 0.127 took: 54.073865 seconds [26] training loss: 0.354 [26, 10] Validation Accuracy: 0.959 [26, 20] Validation Accuracy: 0.959 [26, 30] Validation Accuracy: 0.959 [26, 40] Validation Accuracy: 0.959 [26, 50] Validation Accuracy: 0.959 [26, 60] Validation Accuracy: 0.959 [26, 70] Validation Accuracy: 0.960 [26, 80] Validation Accuracy: 0.960 [26, 90] Validation Accuracy: 0.960 [26, 100] Validation Accuracy: 0.960 [26, 110] Validation Accuracy: 0.960 [26, 120] Validation Accuracy: 0.960 Validation Loss: 0.162 Epoch 26: took: 574.342 seconds Epoch 27: [27, 2000] running loss: 0.195 took: 54.877328 seconds [27, 4000] running loss: 0.124 took: 54.209876 seconds [27, 6000] running loss: 0.124 took: 54.017037 seconds [27, 8000] running loss: 0.123 took: 53.908393 seconds [27, 10000] running loss: 0.123 took: 54.107177 seconds [27, 12000] running loss: 0.124 took: 54.231996 seconds [27, 14000] running loss: 0.124 took: 54.184723 seconds [27, 16000] running loss: 0.123 took: 54.059064 seconds [27, 18000] running loss: 0.123 took: 54.112568 seconds [27, 20000] running loss: 0.123 took: 54.081093 seconds [27] training loss: 0.345 [27, 10] Validation Accuracy: 0.960 [27, 20] Validation Accuracy: 0.960 [27, 30] Validation Accuracy: 0.960 [27, 40] Validation Accuracy: 0.960 [27, 50] Validation Accuracy: 0.961 [27, 60] Validation Accuracy: 0.961 [27, 70] Validation Accuracy: 0.961 [27, 80] Validation Accuracy: 0.961 [27, 90] Validation Accuracy: 0.961 [27, 100] Validation Accuracy: 0.961 [27, 110] Validation Accuracy: 0.961 [27, 120] Validation Accuracy: 0.961 Validation Loss: 0.157 Epoch 27: took: 573.732 seconds Epoch 28: [28, 2000] running loss: 0.190 took: 55.088056 seconds [28, 4000] running loss: 0.119 took: 54.243362 seconds [28, 6000] running loss: 0.120 took: 54.120362 seconds [28, 8000] running loss: 0.121 took: 53.892295 seconds [28, 10000] running loss: 0.122 took: 54.079502 seconds [28, 12000] running loss: 0.122 took: 54.147242 seconds [28, 14000] running loss: 0.122 took: 54.091999 seconds [28, 16000] running loss: 0.118 took: 53.966328 seconds [28, 18000] running loss: 0.120 took: 54.317286 seconds [28, 20000] running loss: 0.121 took: 54.082701 seconds [28] training loss: 0.337 [28, 10] Validation Accuracy: 0.961 [28, 20] Validation Accuracy: 0.961 [28, 30] Validation Accuracy: 0.962 [28, 40] Validation Accuracy: 0.962 [28, 50] Validation Accuracy: 0.962 [28, 60] Validation Accuracy: 0.962 [28, 70] Validation Accuracy: 0.962 [28, 80] Validation Accuracy: 0.962 [28, 90] Validation Accuracy: 0.962 [28, 100] Validation Accuracy: 0.962 [28, 110] Validation Accuracy: 0.962 [28, 120] Validation Accuracy: 0.962 Validation Loss: 0.153 Epoch 28: took: 574.211 seconds Epoch 29: [29, 2000] running loss: 0.186 took: 54.995533 seconds [29, 4000] running loss: 0.120 took: 54.527093 seconds [29, 6000] running loss: 0.119 took: 54.325024 seconds [29, 8000] running loss: 0.119 took: 54.325720 seconds [29, 10000] running loss: 0.117 took: 54.085303 seconds [29, 12000] running loss: 0.120 took: 54.197775 seconds [29, 14000] running loss: 0.121 took: 54.252671 seconds [29, 16000] running loss: 0.118 took: 53.890327 seconds [29, 18000] running loss: 0.117 took: 53.931351 seconds [29, 20000] running loss: 0.114 took: 54.100613 seconds [29] training loss: 0.330 [29, 10] Validation Accuracy: 0.962 [29, 20] Validation Accuracy: 0.963 [29, 30] Validation Accuracy: 0.963 [29, 40] Validation Accuracy: 0.963 [29, 50] Validation Accuracy: 0.963 [29, 60] Validation Accuracy: 0.963 [29, 70] Validation Accuracy: 0.963 [29, 80] Validation Accuracy: 0.963 [29, 90] Validation Accuracy: 0.963 [29, 100] Validation Accuracy: 0.963 [29, 110] Validation Accuracy: 0.963 [29, 120] Validation Accuracy: 0.963 Validation Loss: 0.149 Epoch 29: took: 574.805 seconds Epoch 30: [30, 2000] running loss: 0.179 took: 55.038982 seconds [30, 4000] running loss: 0.117 took: 54.273346 seconds [30, 6000] running loss: 0.115 took: 54.285881 seconds [30, 8000] running loss: 0.118 took: 54.029412 seconds [30, 10000] running loss: 0.115 took: 54.358524 seconds [30, 12000] running loss: 0.116 took: 54.018037 seconds [30, 14000] running loss: 0.116 took: 54.056886 seconds [30, 16000] running loss: 0.116 took: 54.105996 seconds [30, 18000] running loss: 0.118 took: 54.302525 seconds [30, 20000] running loss: 0.116 took: 54.020462 seconds [30] training loss: 0.323 [30, 10] Validation Accuracy: 0.964 [30, 20] Validation Accuracy: 0.964 [30, 30] Validation Accuracy: 0.964 [30, 40] Validation Accuracy: 0.964 [30, 50] Validation Accuracy: 0.964 [30, 60] Validation Accuracy: 0.964 [30, 70] Validation Accuracy: 0.964 [30, 80] Validation Accuracy: 0.964 [30, 90] Validation Accuracy: 0.964 [30, 100] Validation Accuracy: 0.964 [30, 110] Validation Accuracy: 0.964 [30, 120] Validation Accuracy: 0.964 Validation Loss: 0.145 Epoch 30: took: 574.538 seconds Epoch 31: [31, 2000] running loss: 0.178 took: 54.739266 seconds [31, 4000] running loss: 0.115 took: 54.336690 seconds [31, 6000] running loss: 0.115 took: 54.249800 seconds [31, 8000] running loss: 0.113 took: 54.276344 seconds [31, 10000] running loss: 0.115 took: 54.132539 seconds [31, 12000] running loss: 0.112 took: 54.150477 seconds [31, 14000] running loss: 0.115 took: 54.030184 seconds [31, 16000] running loss: 0.113 took: 54.304402 seconds [31, 18000] running loss: 0.112 took: 54.260825 seconds [31, 20000] running loss: 0.113 took: 54.092680 seconds [31] training loss: 0.316 [31, 10] Validation Accuracy: 0.965 [31, 20] Validation Accuracy: 0.965 [31, 30] Validation Accuracy: 0.965 [31, 40] Validation Accuracy: 0.965 [31, 50] Validation Accuracy: 0.965 [31, 60] Validation Accuracy: 0.965 [31, 70] Validation Accuracy: 0.965 [31, 80] Validation Accuracy: 0.965 [31, 90] Validation Accuracy: 0.965 [31, 100] Validation Accuracy: 0.965 [31, 110] Validation Accuracy: 0.965 [31, 120] Validation Accuracy: 0.965 Validation Loss: 0.142 Epoch 31: took: 574.677 seconds Epoch 32: [32, 2000] running loss: 0.172 took: 54.788494 seconds [32, 4000] running loss: 0.114 took: 54.249796 seconds [32, 6000] running loss: 0.113 took: 54.139308 seconds [32, 8000] running loss: 0.110 took: 54.128523 seconds [32, 10000] running loss: 0.111 took: 54.229624 seconds [32, 12000] running loss: 0.112 took: 54.151423 seconds [32, 14000] running loss: 0.114 took: 54.227980 seconds [32, 16000] running loss: 0.113 took: 54.114421 seconds [32, 18000] running loss: 0.113 took: 54.110578 seconds [32, 20000] running loss: 0.111 took: 54.196421 seconds [32] training loss: 0.309 [32, 10] Validation Accuracy: 0.966 [32, 20] Validation Accuracy: 0.966 [32, 30] Validation Accuracy: 0.966 [32, 40] Validation Accuracy: 0.966 [32, 50] Validation Accuracy: 0.966 [32, 60] Validation Accuracy: 0.966 [32, 70] Validation Accuracy: 0.966 [32, 80] Validation Accuracy: 0.966 [32, 90] Validation Accuracy: 0.966 [32, 100] Validation Accuracy: 0.966 [32, 110] Validation Accuracy: 0.966 [32, 120] Validation Accuracy: 0.966 Validation Loss: 0.138 Epoch 32: took: 574.433 seconds Epoch 33: [33, 2000] running loss: 0.173 took: 54.996654 seconds [33, 4000] running loss: 0.112 took: 54.201461 seconds [33, 6000] running loss: 0.111 took: 54.143614 seconds [33, 8000] running loss: 0.111 took: 54.176031 seconds [33, 10000] running loss: 0.110 took: 54.176704 seconds [33, 12000] running loss: 0.110 took: 54.143291 seconds [33, 14000] running loss: 0.108 took: 54.026027 seconds [33, 16000] running loss: 0.111 took: 54.275443 seconds [33, 18000] running loss: 0.111 took: 53.971813 seconds [33, 20000] running loss: 0.109 took: 54.217509 seconds [33] training loss: 0.303 [33, 10] Validation Accuracy: 0.966 [33, 20] Validation Accuracy: 0.966 [33, 30] Validation Accuracy: 0.967 [33, 40] Validation Accuracy: 0.967 [33, 50] Validation Accuracy: 0.967 [33, 60] Validation Accuracy: 0.967 [33, 70] Validation Accuracy: 0.967 [33, 80] Validation Accuracy: 0.967 [33, 90] Validation Accuracy: 0.967 [33, 100] Validation Accuracy: 0.967 [33, 110] Validation Accuracy: 0.967 [33, 120] Validation Accuracy: 0.967 Validation Loss: 0.135 Epoch 33: took: 574.371 seconds Epoch 34: [34, 2000] running loss: 0.165 took: 54.892860 seconds [34, 4000] running loss: 0.109 took: 54.140505 seconds [34, 6000] running loss: 0.108 took: 54.283739 seconds [34, 8000] running loss: 0.109 took: 54.329405 seconds [34, 10000] running loss: 0.109 took: 54.246877 seconds [34, 12000] running loss: 0.109 took: 54.408975 seconds [34, 14000] running loss: 0.107 took: 54.085964 seconds [34, 16000] running loss: 0.109 took: 54.069554 seconds [34, 18000] running loss: 0.108 took: 54.008474 seconds [34, 20000] running loss: 0.110 took: 54.120672 seconds [34] training loss: 0.298 [34, 10] Validation Accuracy: 0.967 [34, 20] Validation Accuracy: 0.967 [34, 30] Validation Accuracy: 0.967 [34, 40] Validation Accuracy: 0.967 [34, 50] Validation Accuracy: 0.968 [34, 60] Validation Accuracy: 0.968 [34, 70] Validation Accuracy: 0.968 [34, 80] Validation Accuracy: 0.968 [34, 90] Validation Accuracy: 0.968 [34, 100] Validation Accuracy: 0.968 [34, 110] Validation Accuracy: 0.968 [34, 120] Validation Accuracy: 0.968 Validation Loss: 0.132 Epoch 34: took: 574.569 seconds Epoch 35: [35, 2000] running loss: 0.166 took: 54.912564 seconds [35, 4000] running loss: 0.109 took: 54.208227 seconds [35, 6000] running loss: 0.108 took: 54.144280 seconds [35, 8000] running loss: 0.105 took: 54.130973 seconds [35, 10000] running loss: 0.106 took: 54.253532 seconds [35, 12000] running loss: 0.106 took: 54.234354 seconds [35, 14000] running loss: 0.106 took: 54.274822 seconds [35, 16000] running loss: 0.106 took: 54.047893 seconds [35, 18000] running loss: 0.106 took: 54.238213 seconds [35, 20000] running loss: 0.107 took: 54.215301 seconds [35] training loss: 0.292 [35, 10] Validation Accuracy: 0.968 [35, 20] Validation Accuracy: 0.968 [35, 30] Validation Accuracy: 0.968 [35, 40] Validation Accuracy: 0.968 [35, 50] Validation Accuracy: 0.968 [35, 60] Validation Accuracy: 0.968 [35, 70] Validation Accuracy: 0.968 [35, 80] Validation Accuracy: 0.968 [35, 90] Validation Accuracy: 0.969 [35, 100] Validation Accuracy: 0.969 [35, 110] Validation Accuracy: 0.969 [35, 120] Validation Accuracy: 0.969 Validation Loss: 0.129 Epoch 35: took: 574.603 seconds Epoch 36: [36, 2000] running loss: 0.165 took: 55.181440 seconds [36, 4000] running loss: 0.108 took: 54.127871 seconds [36, 6000] running loss: 0.107 took: 54.034664 seconds [36, 8000] running loss: 0.106 took: 54.159873 seconds [36, 10000] running loss: 0.105 took: 53.935358 seconds [36, 12000] running loss: 0.105 took: 54.148611 seconds [36, 14000] running loss: 0.103 took: 54.226275 seconds [36, 16000] running loss: 0.105 took: 54.160163 seconds [36, 18000] running loss: 0.107 took: 54.231212 seconds [36, 20000] running loss: 0.103 took: 53.903594 seconds [36] training loss: 0.287 [36, 10] Validation Accuracy: 0.969 [36, 20] Validation Accuracy: 0.969 [36, 30] Validation Accuracy: 0.969 [36, 40] Validation Accuracy: 0.969 [36, 50] Validation Accuracy: 0.969 [36, 60] Validation Accuracy: 0.969 [36, 70] Validation Accuracy: 0.969 [36, 80] Validation Accuracy: 0.969 [36, 90] Validation Accuracy: 0.969 [36, 100] Validation Accuracy: 0.969 [36, 110] Validation Accuracy: 0.969 [36, 120] Validation Accuracy: 0.969 Validation Loss: 0.126 Epoch 36: took: 574.077 seconds Epoch 37: [37, 2000] running loss: 0.161 took: 54.830596 seconds [37, 4000] running loss: 0.105 took: 54.244827 seconds [37, 6000] running loss: 0.107 took: 53.953705 seconds [37, 8000] running loss: 0.106 took: 54.071836 seconds [37, 10000] running loss: 0.105 took: 54.028186 seconds [37, 12000] running loss: 0.104 took: 54.085144 seconds [37, 14000] running loss: 0.103 took: 54.196444 seconds [37, 16000] running loss: 0.104 took: 54.176471 seconds [37, 18000] running loss: 0.105 took: 54.360901 seconds [37, 20000] running loss: 0.104 took: 54.260411 seconds [37] training loss: 0.282 [37, 10] Validation Accuracy: 0.970 [37, 20] Validation Accuracy: 0.970 [37, 30] Validation Accuracy: 0.970 [37, 40] Validation Accuracy: 0.970 [37, 50] Validation Accuracy: 0.970 [37, 60] Validation Accuracy: 0.970 [37, 70] Validation Accuracy: 0.970 [37, 80] Validation Accuracy: 0.970 [37, 90] Validation Accuracy: 0.970 [37, 100] Validation Accuracy: 0.970 [37, 110] Validation Accuracy: 0.970 [37, 120] Validation Accuracy: 0.970 Validation Loss: 0.123 Epoch 37: took: 574.220 seconds Epoch 38: [38, 2000] running loss: 0.161 took: 55.113552 seconds [38, 4000] running loss: 0.105 took: 54.127583 seconds [38, 6000] running loss: 0.105 took: 54.045959 seconds [38, 8000] running loss: 0.103 took: 54.149635 seconds [38, 10000] running loss: 0.103 took: 54.250659 seconds [38, 12000] running loss: 0.104 took: 54.208428 seconds [38, 14000] running loss: 0.101 took: 54.021806 seconds [38, 16000] running loss: 0.102 took: 54.101573 seconds [38, 18000] running loss: 0.104 took: 54.170393 seconds [38, 20000] running loss: 0.102 took: 54.066021 seconds [38] training loss: 0.277 [38, 10] Validation Accuracy: 0.970 [38, 20] Validation Accuracy: 0.970 [38, 30] Validation Accuracy: 0.970 [38, 40] Validation Accuracy: 0.970 [38, 50] Validation Accuracy: 0.970 [38, 60] Validation Accuracy: 0.971 [38, 70] Validation Accuracy: 0.971 [38, 80] Validation Accuracy: 0.971 [38, 90] Validation Accuracy: 0.971 [38, 100] Validation Accuracy: 0.971 [38, 110] Validation Accuracy: 0.971 [38, 120] Validation Accuracy: 0.971 Validation Loss: 0.121 Epoch 38: took: 574.345 seconds Epoch 39: [39, 2000] running loss: 0.156 took: 54.819671 seconds [39, 4000] running loss: 0.103 took: 54.328997 seconds [39, 6000] running loss: 0.102 took: 54.030210 seconds [39, 8000] running loss: 0.102 took: 54.096956 seconds [39, 10000] running loss: 0.101 took: 54.134730 seconds [39, 12000] running loss: 0.100 took: 54.179767 seconds [39, 14000] running loss: 0.102 took: 54.292511 seconds [39, 16000] running loss: 0.103 took: 54.228913 seconds [39, 18000] running loss: 0.101 took: 54.200471 seconds [39, 20000] running loss: 0.104 took: 54.180362 seconds [39] training loss: 0.273 [39, 10] Validation Accuracy: 0.971 [39, 20] Validation Accuracy: 0.971 [39, 30] Validation Accuracy: 0.971 [39, 40] Validation Accuracy: 0.971 [39, 50] Validation Accuracy: 0.971 [39, 60] Validation Accuracy: 0.971 [39, 70] Validation Accuracy: 0.971 [39, 80] Validation Accuracy: 0.971 [39, 90] Validation Accuracy: 0.971 [39, 100] Validation Accuracy: 0.971 [39, 110] Validation Accuracy: 0.971 [39, 120] Validation Accuracy: 0.971 Validation Loss: 0.118 Epoch 39: took: 574.511 seconds Epoch 40: [40, 2000] running loss: 0.158 took: 54.949616 seconds [40, 4000] running loss: 0.101 took: 54.165769 seconds [40, 6000] running loss: 0.101 took: 54.245641 seconds [40, 8000] running loss: 0.102 took: 54.182837 seconds [40, 10000] running loss: 0.100 took: 54.233254 seconds [40, 12000] running loss: 0.099 took: 54.010170 seconds [40, 14000] running loss: 0.101 took: 54.105020 seconds [40, 16000] running loss: 0.101 took: 54.227782 seconds [40, 18000] running loss: 0.101 took: 53.929549 seconds [40, 20000] running loss: 0.099 took: 54.041677 seconds [40] training loss: 0.269 [40, 10] Validation Accuracy: 0.972 [40, 20] Validation Accuracy: 0.972 [40, 30] Validation Accuracy: 0.972 [40, 40] Validation Accuracy: 0.972 [40, 50] Validation Accuracy: 0.972 [40, 60] Validation Accuracy: 0.972 [40, 70] Validation Accuracy: 0.972 [40, 80] Validation Accuracy: 0.972 [40, 90] Validation Accuracy: 0.972 [40, 100] Validation Accuracy: 0.972 [40, 110] Validation Accuracy: 0.972 [40, 120] Validation Accuracy: 0.972 Validation Loss: 0.116 Epoch 40: took: 573.994 seconds Epoch 41: [41, 2000] running loss: 0.152 took: 54.961261 seconds [41, 4000] running loss: 0.100 took: 54.191032 seconds [41, 6000] running loss: 0.101 took: 54.277334 seconds [41, 8000] running loss: 0.100 took: 54.100205 seconds [41, 10000] running loss: 0.099 took: 54.051069 seconds [41, 12000] running loss: 0.101 took: 54.263131 seconds [41, 14000] running loss: 0.099 took: 53.946319 seconds [41, 16000] running loss: 0.100 took: 53.962871 seconds [41, 18000] running loss: 0.097 took: 53.893281 seconds [41, 20000] running loss: 0.098 took: 54.195523 seconds [41] training loss: 0.265 [41, 10] Validation Accuracy: 0.972 [41, 20] Validation Accuracy: 0.972 [41, 30] Validation Accuracy: 0.972 [41, 40] Validation Accuracy: 0.972 [41, 50] Validation Accuracy: 0.972 [41, 60] Validation Accuracy: 0.972 [41, 70] Validation Accuracy: 0.972 [41, 80] Validation Accuracy: 0.972 [41, 90] Validation Accuracy: 0.973 [41, 100] Validation Accuracy: 0.973 [41, 110] Validation Accuracy: 0.973 [41, 120] Validation Accuracy: 0.973 Validation Loss: 0.114 Epoch 41: took: 573.888 seconds Epoch 42: [42, 2000] running loss: 0.153 took: 55.011995 seconds [42, 4000] running loss: 0.099 took: 54.273586 seconds [42, 6000] running loss: 0.099 took: 54.065748 seconds [42, 8000] running loss: 0.099 took: 54.302555 seconds [42, 10000] running loss: 0.097 took: 54.067539 seconds [42, 12000] running loss: 0.098 took: 54.103320 seconds [42, 14000] running loss: 0.099 took: 54.193188 seconds [42, 16000] running loss: 0.098 took: 54.167322 seconds [42, 18000] running loss: 0.097 took: 54.131631 seconds [42, 20000] running loss: 0.097 took: 54.390706 seconds [42] training loss: 0.261 [42, 10] Validation Accuracy: 0.973 [42, 20] Validation Accuracy: 0.973 [42, 30] Validation Accuracy: 0.973 [42, 40] Validation Accuracy: 0.973 [42, 50] Validation Accuracy: 0.973 [42, 60] Validation Accuracy: 0.973 [42, 70] Validation Accuracy: 0.973 [42, 80] Validation Accuracy: 0.973 [42, 90] Validation Accuracy: 0.973 [42, 100] Validation Accuracy: 0.973 [42, 110] Validation Accuracy: 0.973 [42, 120] Validation Accuracy: 0.973 Validation Loss: 0.112 Epoch 42: took: 574.677 seconds Epoch 43: [43, 2000] running loss: 0.152 took: 54.936108 seconds [43, 4000] running loss: 0.098 took: 54.166475 seconds [43, 6000] running loss: 0.097 took: 54.145926 seconds [43, 8000] running loss: 0.097 took: 54.182619 seconds [43, 10000] running loss: 0.098 took: 54.321356 seconds [43, 12000] running loss: 0.098 took: 54.261150 seconds [43, 14000] running loss: 0.097 took: 54.190407 seconds [43, 16000] running loss: 0.098 took: 54.206162 seconds [43, 18000] running loss: 0.097 took: 54.226556 seconds [43, 20000] running loss: 0.100 took: 54.227798 seconds [43] training loss: 0.257 [43, 10] Validation Accuracy: 0.973 [43, 20] Validation Accuracy: 0.973 [43, 30] Validation Accuracy: 0.973 [43, 40] Validation Accuracy: 0.973 [43, 50] Validation Accuracy: 0.973 [43, 60] Validation Accuracy: 0.973 [43, 70] Validation Accuracy: 0.974 [43, 80] Validation Accuracy: 0.974 [43, 90] Validation Accuracy: 0.974 [43, 100] Validation Accuracy: 0.974 [43, 110] Validation Accuracy: 0.974 [43, 120] Validation Accuracy: 0.974 Validation Loss: 0.110 Epoch 43: took: 574.933 seconds Epoch 44: [44, 2000] running loss: 0.146 took: 54.955018 seconds [44, 4000] running loss: 0.099 took: 54.116808 seconds [44, 6000] running loss: 0.097 took: 54.005776 seconds [44, 8000] running loss: 0.097 took: 54.090370 seconds [44, 10000] running loss: 0.097 took: 54.006252 seconds [44, 12000] running loss: 0.098 took: 53.964926 seconds [44, 14000] running loss: 0.097 took: 54.019566 seconds [44, 16000] running loss: 0.096 took: 54.099744 seconds [44, 18000] running loss: 0.097 took: 53.946722 seconds [44, 20000] running loss: 0.096 took: 54.189907 seconds [44] training loss: 0.253 [44, 10] Validation Accuracy: 0.974 [44, 20] Validation Accuracy: 0.974 [44, 30] Validation Accuracy: 0.974 [44, 40] Validation Accuracy: 0.974 [44, 50] Validation Accuracy: 0.974 [44, 60] Validation Accuracy: 0.974 [44, 70] Validation Accuracy: 0.974 [44, 80] Validation Accuracy: 0.974 [44, 90] Validation Accuracy: 0.974 [44, 100] Validation Accuracy: 0.974 [44, 110] Validation Accuracy: 0.974 [44, 120] Validation Accuracy: 0.974 Validation Loss: 0.108 Epoch 44: took: 573.360 seconds Epoch 45: [45, 2000] running loss: 0.147 took: 54.912267 seconds [45, 4000] running loss: 0.098 took: 54.272042 seconds [45, 6000] running loss: 0.095 took: 54.239315 seconds [45, 8000] running loss: 0.096 took: 53.939127 seconds
In [39]:
# Check dataloaders have about the same sizes
print(len(train_loader) * batch_size)
print(len(validation_loader) * batch_size)
1348736 7872
In [41]:
print("Training Accuracy per Epoch: ")
print(training_accuracy_epoch)
Training Accuracy per Epoch:
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In [42]:
print("Running Losses per Epoch: ")
print(running_losses_epoch)
Running Losses per Epoch:
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In [43]:
print("Training Losses per Epoch: ")
print(training_losses_epoch)
Training Losses per Epoch:
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In [44]:
print("Validation Losses per Epoch: ")
print(val_losses_epoch)
Validation Losses per Epoch:
{0: [0.1806653093482973, 1.0614973880660319, 0.8443645154508636, 0.7149525179421528, 0.6173405072760351, 0.5411769994224587, 0.4812125880456463, 0.43346169025132614, 0.39490071685404704, 0.3643318631799832, 0.3376575188169261, 0.3150628462982447, 0.2955364056362591, 0.2779867451242528, 0.2624383342339728, 0.249120063329953, 0.23721377203004418, 0.22630373425427122, 0.21646795469638683, 0.2074494333210823, 0.19918178853238497, 0.19174778363926498, 0.18485254165027115, 0.17842476071964739, 0.17253566325104697, 0.16704149591307293, 0.16209541579388423, 0.1574760657739242, 0.15319249637418805, 0.1491015901507434, 0.14526462134967943, 0.1415664351889811, 0.13808593814078307, 0.13466944911986137, 0.1318911418606513, 0.12890389616586137, 0.12602070791801767, 0.12333891688353078, 0.1207916484260826, 0.11835462717246556, 0.11606802879114479, 0.11391088149043467, 0.11180555124626614, 0.10980847770739041, 0.10783615059688018, 0.10595760314857744, 0.10414437882162551, 0.10245355948762813, 0.1008278630953577, 0.09920715920141739, 0.09768864596281576, 0.09625360457611913, 0.09490391854235979, 0.09352105158379537, 0.09222276947812011, 0.09093752988527101, 0.0897485239287061, 0.08855919002783952, 0.08744016447612965, 0.08631515964014053, 0.0852364155007516, 0.0841940657799983, 0.0832188362687369, 0.08222171986488187, 0.08124936807316913, 0.08033103910637938, 0.07944220567789495, 0.0785343597800058, 0.07769364295979084, 0.07689440698499482, 0.07608499834426133, 0.07530044532713474, 0.07454215323587883, 0.07379054169281465, 0.07309847969642241, 0.07240507377413484, 0.07174569199691472, 0.07109152002792751, 0.07045654787970224, 0.06984658127591097, 0.06921589118302716, 0.06862882427258302, 0.06806132963269865, 0.0674713495786876, 0.06689065683864229, 0.06634696743103495, 0.06580989055766438, 0.065289378252759, 0.0647550673203686, 0.06425199448594526, 0.06376735293451563, 0.06329436808111938, 0.06279962880833335, 0.06232527911269577, 0.06188983980766004, 0.061447871180714805, 0.06099207608331389, 0.06057698376533457, 0.06016624572272596, 0.05977223951698655], 1: [0.1806653093482973, 1.0614973880660319, 0.8443645154508636, 0.7149525179421528, 0.6173405072760351, 0.5411769994224587, 0.4812125880456463, 0.43346169025132614, 0.39490071685404704, 0.3643318631799832, 0.3376575188169261, 0.3150628462982447, 0.2955364056362591, 0.2779867451242528, 0.2624383342339728, 0.249120063329953, 0.23721377203004418, 0.22630373425427122, 0.21646795469638683, 0.2074494333210823, 0.19918178853238497, 0.19174778363926498, 0.18485254165027115, 0.17842476071964739, 0.17253566325104697, 0.16704149591307293, 0.16209541579388423, 0.1574760657739242, 0.15319249637418805, 0.1491015901507434, 0.14526462134967943, 0.1415664351889811, 0.13808593814078307, 0.13466944911986137, 0.1318911418606513, 0.12890389616586137, 0.12602070791801767, 0.12333891688353078, 0.1207916484260826, 0.11835462717246556, 0.11606802879114479, 0.11391088149043467, 0.11180555124626614, 0.10980847770739041, 0.10783615059688018, 0.10595760314857744, 0.10414437882162551, 0.10245355948762813, 0.1008278630953577, 0.09920715920141739, 0.09768864596281576, 0.09625360457611913, 0.09490391854235979, 0.09352105158379537, 0.09222276947812011, 0.09093752988527101, 0.0897485239287061, 0.08855919002783952, 0.08744016447612965, 0.08631515964014053, 0.0852364155007516, 0.0841940657799983, 0.0832188362687369, 0.08222171986488187, 0.08124936807316913, 0.08033103910637938, 0.07944220567789495, 0.0785343597800058, 0.07769364295979084, 0.07689440698499482, 0.07608499834426133, 0.07530044532713474, 0.07454215323587883, 0.07379054169281465, 0.07309847969642241, 0.07240507377413484, 0.07174569199691472, 0.07109152002792751, 0.07045654787970224, 0.06984658127591097, 0.06921589118302716, 0.06862882427258302, 0.06806132963269865, 0.0674713495786876, 0.06689065683864229, 0.06634696743103495, 0.06580989055766438, 0.065289378252759, 0.0647550673203686, 0.06425199448594526, 0.06376735293451563, 0.06329436808111938, 0.06279962880833335, 0.06232527911269577, 0.06188983980766004, 0.061447871180714805, 0.06099207608331389, 0.06057698376533457, 0.06016624572272596, 0.05977223951698655], 2: [0.1806653093482973, 1.0614973880660319, 0.8443645154508636, 0.7149525179421528, 0.6173405072760351, 0.5411769994224587, 0.4812125880456463, 0.43346169025132614, 0.39490071685404704, 0.3643318631799832, 0.3376575188169261, 0.3150628462982447, 0.2955364056362591, 0.2779867451242528, 0.2624383342339728, 0.249120063329953, 0.23721377203004418, 0.22630373425427122, 0.21646795469638683, 0.2074494333210823, 0.19918178853238497, 0.19174778363926498, 0.18485254165027115, 0.17842476071964739, 0.17253566325104697, 0.16704149591307293, 0.16209541579388423, 0.1574760657739242, 0.15319249637418805, 0.1491015901507434, 0.14526462134967943, 0.1415664351889811, 0.13808593814078307, 0.13466944911986137, 0.1318911418606513, 0.12890389616586137, 0.12602070791801767, 0.12333891688353078, 0.1207916484260826, 0.11835462717246556, 0.11606802879114479, 0.11391088149043467, 0.11180555124626614, 0.10980847770739041, 0.10783615059688018, 0.10595760314857744, 0.10414437882162551, 0.10245355948762813, 0.1008278630953577, 0.09920715920141739, 0.09768864596281576, 0.09625360457611913, 0.09490391854235979, 0.09352105158379537, 0.09222276947812011, 0.09093752988527101, 0.0897485239287061, 0.08855919002783952, 0.08744016447612965, 0.08631515964014053, 0.0852364155007516, 0.0841940657799983, 0.0832188362687369, 0.08222171986488187, 0.08124936807316913, 0.08033103910637938, 0.07944220567789495, 0.0785343597800058, 0.07769364295979084, 0.07689440698499482, 0.07608499834426133, 0.07530044532713474, 0.07454215323587883, 0.07379054169281465, 0.07309847969642241, 0.07240507377413484, 0.07174569199691472, 0.07109152002792751, 0.07045654787970224, 0.06984658127591097, 0.06921589118302716, 0.06862882427258302, 0.06806132963269865, 0.0674713495786876, 0.06689065683864229, 0.06634696743103495, 0.06580989055766438, 0.065289378252759, 0.0647550673203686, 0.06425199448594526, 0.06376735293451563, 0.06329436808111938, 0.06279962880833335, 0.06232527911269577, 0.06188983980766004, 0.061447871180714805, 0.06099207608331389, 0.06057698376533457, 0.06016624572272596, 0.05977223951698655], 3: [0.1806653093482973, 1.0614973880660319, 0.8443645154508636, 0.7149525179421528, 0.6173405072760351, 0.5411769994224587, 0.4812125880456463, 0.43346169025132614, 0.39490071685404704, 0.3643318631799832, 0.3376575188169261, 0.3150628462982447, 0.2955364056362591, 0.2779867451242528, 0.2624383342339728, 0.249120063329953, 0.23721377203004418, 0.22630373425427122, 0.21646795469638683, 0.2074494333210823, 0.19918178853238497, 0.19174778363926498, 0.18485254165027115, 0.17842476071964739, 0.17253566325104697, 0.16704149591307293, 0.16209541579388423, 0.1574760657739242, 0.15319249637418805, 0.1491015901507434, 0.14526462134967943, 0.1415664351889811, 0.13808593814078307, 0.13466944911986137, 0.1318911418606513, 0.12890389616586137, 0.12602070791801767, 0.12333891688353078, 0.1207916484260826, 0.11835462717246556, 0.11606802879114479, 0.11391088149043467, 0.11180555124626614, 0.10980847770739041, 0.10783615059688018, 0.10595760314857744, 0.10414437882162551, 0.10245355948762813, 0.1008278630953577, 0.09920715920141739, 0.09768864596281576, 0.09625360457611913, 0.09490391854235979, 0.09352105158379537, 0.09222276947812011, 0.09093752988527101, 0.0897485239287061, 0.08855919002783952, 0.08744016447612965, 0.08631515964014053, 0.0852364155007516, 0.0841940657799983, 0.0832188362687369, 0.08222171986488187, 0.08124936807316913, 0.08033103910637938, 0.07944220567789495, 0.0785343597800058, 0.07769364295979084, 0.07689440698499482, 0.07608499834426133, 0.07530044532713474, 0.07454215323587883, 0.07379054169281465, 0.07309847969642241, 0.07240507377413484, 0.07174569199691472, 0.07109152002792751, 0.07045654787970224, 0.06984658127591097, 0.06921589118302716, 0.06862882427258302, 0.06806132963269865, 0.0674713495786876, 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0.13808593814078307, 0.13466944911986137, 0.1318911418606513, 0.12890389616586137, 0.12602070791801767, 0.12333891688353078, 0.1207916484260826, 0.11835462717246556, 0.11606802879114479, 0.11391088149043467, 0.11180555124626614, 0.10980847770739041, 0.10783615059688018, 0.10595760314857744, 0.10414437882162551, 0.10245355948762813, 0.1008278630953577, 0.09920715920141739, 0.09768864596281576, 0.09625360457611913, 0.09490391854235979, 0.09352105158379537, 0.09222276947812011, 0.09093752988527101, 0.0897485239287061, 0.08855919002783952, 0.08744016447612965, 0.08631515964014053, 0.0852364155007516, 0.0841940657799983, 0.0832188362687369, 0.08222171986488187, 0.08124936807316913, 0.08033103910637938, 0.07944220567789495, 0.0785343597800058, 0.07769364295979084, 0.07689440698499482, 0.07608499834426133, 0.07530044532713474, 0.07454215323587883, 0.07379054169281465, 0.07309847969642241, 0.07240507377413484, 0.07174569199691472, 0.07109152002792751, 0.07045654787970224, 0.06984658127591097, 0.06921589118302716, 0.06862882427258302, 0.06806132963269865, 0.0674713495786876, 0.06689065683864229, 0.06634696743103495, 0.06580989055766438, 0.065289378252759, 0.0647550673203686, 0.06425199448594526, 0.06376735293451563, 0.06329436808111938, 0.06279962880833335, 0.06232527911269577, 0.06188983980766004, 0.061447871180714805, 0.06099207608331389, 0.06057698376533457, 0.06016624572272596, 0.05977223951698655], 5: [0.1806653093482973, 1.0614973880660319, 0.8443645154508636, 0.7149525179421528, 0.6173405072760351, 0.5411769994224587, 0.4812125880456463, 0.43346169025132614, 0.39490071685404704, 0.3643318631799832, 0.3376575188169261, 0.3150628462982447, 0.2955364056362591, 0.2779867451242528, 0.2624383342339728, 0.249120063329953, 0.23721377203004418, 0.22630373425427122, 0.21646795469638683, 0.2074494333210823, 0.19918178853238497, 0.19174778363926498, 0.18485254165027115, 0.17842476071964739, 0.17253566325104697, 0.16704149591307293, 0.16209541579388423, 0.1574760657739242, 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In [72]:
### Visualizing
fig, ax = plt.subplots()
x_val_loss = np.arange(1, 100, 1)
y_val_loss = val_losses_epoch[99][1:]
ax.plot(x_val_loss, y_val_loss)
ax.set(xlabel='Epoch #', ylabel='Validation Losses',
title='Validation Losses')
ax.grid()
fig.savefig("validation_loss.png")
plt.show()
In [92]:
### Visualizing
fig, ax = plt.subplots()
x_val_acc = np.arange(1, 100, 1/12)
y_val_acc = training_accuracy_epoch[99][12:]
ax.plot(x_val_acc, y_val_acc)
ax.set(xlabel='Epoch #', ylabel='Validation Accuracy (%)',
title='Validation Accuracy')
ax.grid()
fig.savefig("validation_accuracy.png")
plt.show()
In [93]:
# Make Testset
test_images, test_labels = readTrafficSigns(test_path)
test_dataset = TrafficSignDataset(test_images, test_labels)
print(len(test_dataset))
showLabelDistribution(test_dataset)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, num_workers=4)
26640
{0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, 37: 0, 38: 0, 39: 0, 40: 0, 41: 0, 42: 0}
{0: 150, 1: 1500, 2: 1500, 3: 960, 4: 1320, 5: 1260, 6: 300, 7: 960, 8: 960, 9: 990, 10: 1350, 11: 900, 12: 1410, 13: 1440, 14: 540, 15: 420, 16: 300, 17: 750, 18: 810, 19: 150, 20: 240, 21: 240, 22: 270, 23: 360, 24: 180, 25: 1020, 26: 420, 27: 180, 28: 360, 29: 180, 30: 300, 31: 540, 32: 180, 33: 480, 34: 300, 35: 810, 36: 270, 37: 150, 38: 1380, 39: 210, 40: 240, 41: 180, 42: 180}
In [95]:
len(test_loader)
Out[95]:
417
In [97]:
# Test against testset
net.eval()
correct_test = 0
total_test = 0
with torch.no_grad():
for i, (images, labels) in enumerate(test_loader):
images, labels = images.to(device), labels.to(device)
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total_test += labels.size(0)
correct_test += (predicted == labels).sum().item()
if i % 50 == 49:
test_accuracy = np.sum(correct_test) / total_test
print('[%5d] Test Accuracy: %.3f \t' % (i + 1, test_accuracy))
print('Accuracy of the network on the 26640 test images: %.6f %%' % (
100 * correct_test / total_test))
[ 50] Test Accuracy: 0.997 [ 100] Test Accuracy: 0.996 [ 150] Test Accuracy: 0.996 [ 200] Test Accuracy: 0.996 [ 250] Test Accuracy: 0.997 [ 300] Test Accuracy: 0.997 [ 350] Test Accuracy: 0.998 [ 400] Test Accuracy: 0.998 Accuracy of the network on the 26640 test images: 99.786036 %
In [ ]: